Watershed Geovisualization

Lesson Goals

In this lesson, we demonstrate several approaches towards a particular subset of geovisualization methods, namely choropleth maps. We start with an exploratory workflow introducing mapclassify and geopandas to create different choropleth classifications and maps for quick exploratory data analysis. We then introduce the geoviews package for interactive mapping in support of exploratory spatial data analysis.

Prerequests

  1. Install geopandas
  2. install pySal

    NB:make sure you start your work with new directory with all library

PySAL Map Classifiers

As a first cut, geopandas makes it very easy to plot a map quickly. If you know the area well, this may do fine for quick exploration. If you don't know a place extremely well (or you want to make a figure easy to understand for those who don't) it's often a good idea to add a basemap for context. We can do that easily using the contextily package

GeoPandas: Choropleths

Further reading

  1. Beyond choropleth maps: A review of techniques to visualize quantitative areal geodata link.

  2. Dynamic Choropleth Maps - Using Amalgamation to Increase Area Perceivability link

  3. Exploring the Sensitivity of Choropleths under Attribute Uncertainty link.


Conclusion

This lesson has presented geovisualization techniques for the visualization of spatial data. In this lesson you learn the role of map classification in exploratory data analysis and producing choropleth maps. Additionally, you also learn how to use different tools include graphs representing statistical distribution of attribute values, calculation of statistical quality of a classification, and various color schemes that can be applied to represent classes on a choropleth map.